615 research outputs found
Ultrafast electron diffraction using an ultracold source
We present diffraction patterns from micron-sized areas of mono-crystalline
graphite obtained with an ultracold and ultrafast electron source. We show that
high spatial coherence is manifest in the visibility of the patterns even for
picosecond bunches of appreciable charge, enabled by the extremely low source
temperature (~ 10 K). For a larger, ~ 100 um spot size on the sample, spatial
coherence lengths > 10 nm result, sufficient to resolve diffraction patterns of
complex protein crystals. This makes the source ideal for ultrafast electron
diffraction of complex macromolecular structures such as membrane proteins, in
a regime unattainable by conventional photocathode sources. By further reducing
the source size, sub-um spot sizes on the sample become possible with spatial
coherence lengths exceeding 1 nm, enabling ultrafast nano-diffraction for
material science.Comment: 5 pages, 4 figure
A Solvable Model of Secondary Structure Formation in Random Hetero-Polymers
We propose and solve a simple model describing secondary structure formation
in random hetero-polymers. It describes monomers with a combination of
one-dimensional short-range interactions (representing steric forces and
hydrogen bonds) and infinite range interactions (representing polarity forces).
We solve our model using a combination of mean field and random field
techniques, leading to phase diagrams exhibiting second-order transitions
between folded, partially folded and unfolded states, including regions where
folding depends on initial conditions. Our theoretical results, which are in
excellent agreement with numerical simulations, lead to an appealing physical
picture of the folding process: the polarity forces drive the transition to a
collapsed state, the steric forces introduce monomer specificity, and the
hydrogen bonds stabilise the conformation by damping the frustration-induced
multiplicity of states.Comment: 24 pages, 14 figure
Hierarchical Self-Programming in Recurrent Neural Networks
We study self-programming in recurrent neural networks where both neurons
(the `processors') and synaptic interactions (`the programme') evolve in time
simultaneously, according to specific coupled stochastic equations. The
interactions are divided into a hierarchy of groups with adiabatically
separated and monotonically increasing time-scales, representing sub-routines
of the system programme of decreasing volatility. We solve this model in
equilibrium, assuming ergodicity at every level, and find as our
replica-symmetric solution a formalism with a structure similar but not
identical to Parisi's -step replica symmetry breaking scheme. Apart from
differences in details of the equations (due to the fact that here
interactions, rather than spins, are grouped into clusters with different
time-scales), in the present model the block sizes of the emerging
ultrametric solution are not restricted to the interval , but are
independent control parameters, defined in terms of the noise strengths of the
various levels in the hierarchy, which can take any value in [0,\infty\ket.
This is shown to lead to extremely rich phase diagrams, with an abundance of
first-order transitions especially when the level of stochasticity in the
interaction dynamics is chosen to be low.Comment: 53 pages, 19 figures. Submitted to J. Phys.
Video-Assisted Thoracoscopic Surgery in Patients With Clinically Resectable Lung Tumors
To investigate the feasibility of thoracoscopic resection, a pilot study was performed in patients with clinically resectable lung tumors. In 40 patients, Video-assisted thoracic surgery (VATS) was performed because of suspicion of malignancy. There were 29 men and 11 women with a median age of 54.8 years (range 18 to 78). Preoperative indications were suspected lung cancer and tumor in 27 patients, assessment of tumor resectability in 7 patients, and probability of metastatic tumors in 6 patients. The final diagnoses in the 27 patients with suspected lung cancer were 12 primary lung cancers, 6 lung metastases, and 9 benign lesions. The success rates for VATS (no conversion to thoracotomy) were 1 of 12 (8.3%) for resectable stage I lung cancer, 8 of 12 (66.7%) for metastatic tumors, and 9 of 9 (100%) for benign tumors. With VATS, 6 of 7 patients (85.7%), possible stage III non-small cell lung cancer, an explorative thoracotomy with was avoided, significantly reducing morbidity. The reasons for conversion to thoracotomy were 1) oncological (N2 lymph node dissection and prevention of tumor spillage) and 2) technical (inability to locate the nodule, central localization, no anatomical fissure, or poor lung function requiring full lung ventilation). The ultimate diagnoses were 19 lung cancers, 12 metastatic lung tumors, and 9 benign lung tumors. Our data show the limitations of VATS for malignant tumors in general use. These findings, together with the fact that experience in performing thoracoscopic procedures demonstrates a learning curve, may limit the use of thoracoscopic resection as a routine surgical procedure, especially when strict oncological rules are respected
Randomized controlled trial comparing magnetic marker localization (MaMaLoc) with wire-guided localization in the treatment of early-stage breast cancer
Wire-guided localization (WGL) is the standard of care in the surgical treatment of nonpalpable breast tumors. In this study, we compare the use of a new magnetic marker localization (MaMaLoc) technique to WGL in the treatment of early-stage breast cancer patients. Open-label, single-center, randomized controlled trial comparing MaMaLoc (intervention) to WGL (control) in women with early-stage breast cancer. Primary outcome was surgical usability measured using the System Usability Scale (SUS, 0-100 score). Secondary outcomes were patient reported, clinical, and pathological outcomes such as retrieval rate, operative time, resected specimen weight, margin status, and reoperation rate. Thirty-two patients were analyzed in the MaMaLoc group and 35 in the WGL group. Patient and tumor characteristics were comparable between groups. No in situ complications occurred. Retrieval rate was 100% in both groups. Surgical usability was higher for MaMaLoc: 70.2 ± 8.9 vs. 58.1 ± 9.1, p < 0.001. Patients reported higher overall satisfaction with MaMaLoc (median score 5/5) versus WGL (score 4/5), p < 0.001. The use of magnetic marker localization (MaMaLoc) for early-stage breast cancer is effective and has higher surgical usability than standard WGL
Polynomial iterative algorithms for coloring and analyzing random graphs
We study the graph coloring problem over random graphs of finite average
connectivity . Given a number of available colors, we find that graphs
with low connectivity admit almost always a proper coloring whereas graphs with
high connectivity are uncolorable. Depending on , we find the precise value
of the critical average connectivity . Moreover, we show that below
there exist a clustering phase in which ground states
spontaneously divide into an exponential number of clusters. Furthermore, we
extended our considerations to the case of single instances showing consistent
results. This lead us to propose a new algorithm able to color in polynomial
time random graphs in the hard but colorable region, i.e when .Comment: 23 pages, 10 eps figure
Slowly evolving geometry in recurrent neural networks I: extreme dilution regime
We study extremely diluted spin models of neural networks in which the
connectivity evolves in time, although adiabatically slowly compared to the
neurons, according to stochastic equations which on average aim to reduce
frustration. The (fast) neurons and (slow) connectivity variables equilibrate
separately, but at different temperatures. Our model is exactly solvable in
equilibrium. We obtain phase diagrams upon making the condensed ansatz (i.e.
recall of one pattern). These show that, as the connectivity temperature is
lowered, the volume of the retrieval phase diverges and the fraction of
mis-aligned spins is reduced. Still one always retains a region in the
retrieval phase where recall states other than the one corresponding to the
`condensed' pattern are locally stable, so the associative memory character of
our model is preserved.Comment: 18 pages, 6 figure
On the freezing of variables in random constraint satisfaction problems
The set of solutions of random constraint satisfaction problems (zero energy
groundstates of mean-field diluted spin glasses) undergoes several structural
phase transitions as the amount of constraints is increased. This set first
breaks down into a large number of well separated clusters. At the freezing
transition, which is in general distinct from the clustering one, some
variables (spins) take the same value in all solutions of a given cluster. In
this paper we study the critical behavior around the freezing transition, which
appears in the unfrozen phase as the divergence of the sizes of the
rearrangements induced in response to the modification of a variable. The
formalism is developed on generic constraint satisfaction problems and applied
in particular to the random satisfiability of boolean formulas and to the
coloring of random graphs. The computation is first performed in random tree
ensembles, for which we underline a connection with percolation models and with
the reconstruction problem of information theory. The validity of these results
for the original random ensembles is then discussed in the framework of the
cavity method.Comment: 32 pages, 7 figure
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